The underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems in the Northern and Eastern United States

被引:11
作者
Gu, Yating [1 ]
Zhao, Yingyi [1 ]
Guo, Zhengfei [1 ]
Meng, Lin [2 ]
Zhang, Kun [1 ]
Wang, Jing [1 ,3 ]
Lee, Calvin K. F. [1 ]
Xie, Jing [4 ]
Wang, Yantian [5 ]
Yan, Zhengbing [1 ,6 ]
Zhang, He [1 ]
Wu, Jin [1 ,7 ,8 ]
机构
[1] Univ Hong Kong, Sch Biol Sci, Res Area Ecol & Biodivers, Hong Kong, Peoples R China
[2] Vanderbilt Univ, Dept Earth & Environm Sci, Nashville, TN USA
[3] Sun Yat Sen Univ, Sch Ecol, Guangzhou 510275, Peoples R China
[4] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Peoples R China
[5] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
[6] Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China
[7] Univ Hong Kong, Inst Climate & Carbon Neutral, Hong Kong, Peoples R China
[8] Univ Hong Kong, Sch Biol Sci, Pokfulam Rd, Hong Kong, Peoples R China
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
Land surface phenology; Leaf unfolding date; Trophic interactions; Climate feedback; Optimized trade-off strategy; Optimal photosynthesis gain; TREE PHENOLOGY; CLIMATE-CHANGE; MODEL; RESPONSES; DYNAMICS; PHOTOPERIOD; PREDICTION; AUTUMN; LENGTH; EARTH;
D O I
10.1016/j.rse.2023.113617
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Spring phenology of temperate ecosystems is highly sensitive to climate change, generating various impacts on many important terrestrial surface biophysical processes. Although various prognostic models relying on environmental variables of temperature and photoperiod have been developed for spring phenology, comprehensive ecosystem-scale evaluations over large landscapes and long-time periods remain lacking. Further, environmental variables other than temperature and photoperiod might also importantly constrain spring phenology modelling but remain under-investigation. To address these issues, we leveraged around 20-years datasets of environmental variables (from Daymet and GLDAS products) and the spring phenology metric (i.e., the greenup date) respectively derived from MODIS and PhenoCams across 108 sites in the Northern and Eastern United States. We firstly cross-compared MODIS-derived greenup date with official PhenoCams product with high accuracy (R2 = 0.70). Then, we evaluated the three prognostic models (i.e., Growing Degree Date (GDD), Sequential (SEQ) and optimality-based (OPT)) with MODIS-derived spring phenology, assessed the model residuals and their associations with soil moisture, rainfall, and solar radiation, and revised the two photoperiod-relevant models (SEQ, OPT) by replacing the daylength variable with solar radiation, which was found to contribute the most to model residuals. We found that 1) all models demonstrated good capability in characterizing spring phenology, with OPT performing the best (RMSE = 8.04 +/- 5.05 days), followed by SEQ (RMSE = 10.57 +/- 7.77 days) and GDD (RMSE = 10.84 +/- 8.42 days), 2) all models displayed high model residuals showing tight correlation with solar radiation (r = 0.45-0.75), and 3) the revised models that included solar radiation significantly performed better with an RMSE reduction by 22.08%. Such results are likely because solar radiation better constrains early growing season plant photosynthesis than photoperiod, supporting the hypothesis of spring phenology as an adaptive strategy to maximize photosynthetic carbon gain (approximated by solar radiation) while minimizing frost damage risk (captured by temperature). Collectively, our study reveals the underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems, and suggests ways to improve spring phenology modelling and other phenology-related ecological processes.
引用
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页数:15
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